A Clinical Perspective on Big Data in Mental Health

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.

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Innovation of Self-service System of College Mental Health in the Age of Big Data

Advances in Intelligent Systems and Computing - Big Data Analytics for Cyber-Physical System in Smart City ◽

10.1007/978-981-15-2568-1_59 ◽

2020 ◽

pp. 429-435

Author(s):

Tiantian Zhang ◽

Xu Chen

Keyword(s):

Mental Health ◽

Big Data ◽

Service System ◽

College Mental Health ◽

Self Service

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Mental Health Clinical Decision Support Exploiting Big Data

Computational Methods and Algorithms for Medicine and Optimized Clinical Practice - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8244-1.ch008 ◽

2019 ◽

pp. 160-184 ◽

Cited By ~ 1

Author(s):

Jan Kalina

Keyword(s):

Mental Health ◽

Decision Making ◽

Big Data ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Decision Making ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

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Big data in mental health research – do the ns justify the means? Using large data-sets of electronic health records for mental health research

BJPsych Bulletin ◽

10.1192/pb.bp.116.055053 ◽

2017 ◽

Vol 41 (3) ◽

pp. 129-132 ◽

Cited By ~ 9

Author(s):

Peter Schofield

Keyword(s):

Mental Health ◽

Big Data ◽

Data Storage ◽

Health Research ◽

Large Data ◽

Population Data ◽

Mental Health Research ◽

Multiple Time ◽

Short Account ◽

Electronic Health

SummaryAdvances in information technology and data storage, so-called ‘big data’, have the potential to dramatically change the way we do research. We are presented with the possibility of whole-population data, collected over multiple time points and including detailed demographic information usually only available in expensive and labour-intensive surveys, but at a fraction of the cost and effort. Typically, accounts highlight the sheer volume of data available in terms of terabytes (1012) and petabytes (1015) of data while charting the exponential growth in computing power we can use to make sense of this. Presented with resources of such dizzying magnitude it is easy to lose sight of the potential limitations when the amount of data itself appears unlimited. In this short account I look at some recent advances in electronic health data that are relevant for mental health research while highlighting some of the potential pitfalls.

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Invited Commentary on Stewart and Davis “ ‘Big data’ in mental health research—current status and emerging possibilities”

Social Psychiatry and Psychiatric Epidemiology ◽

10.1007/s00127-016-1294-4 ◽

2016 ◽

Vol 52 (2) ◽

pp. 127-129 ◽

Cited By ~ 1

Author(s):

Jonathan D. Hafferty ◽

Daniel J. Smith ◽

Andrew M. McIntosh

Keyword(s):

Mental Health ◽

Big Data ◽

Health Research ◽

Mental Health Research ◽

Current Status

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Analysis of Influencing Factors of Social Mental Health Based on Big Data

Mobile Information Systems ◽

10.1155/2021/9969399 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Yixuan Zhao ◽

Qinghua Tang

Keyword(s):

Mental Health ◽

Big Data ◽

Research Methods ◽

Influencing Factors ◽

Large Scale ◽

Huge Amount ◽

Economic Market ◽

Huge Data ◽

Proposed Model ◽

Correlation Degree

Big data is a large-scale rapidly growing database of information. Big data has a huge data size and complexity that cannot be easily stored or processed by conventional data processing tools. Big data research methods have been widely used in many disciplines as research methods based on massively big data analysis have aroused great interest in scientific methodology. In this paper, we proposed a deep computational model to analyze the factors that affect social and mental health. The proposed model utilizes a large number of microblog manual annotation datasets. This huge amount of dataset is divided into six main factors that affect social and mental health, that is, economic market correlation, the political democracy, the management law, the cultural trend, the expansion of the information level, and the fast correlation of the rhythm of life. The proposed model compares the review data of different influencing factors to get the correlation degree between social mental health and these factors.

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